# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import os.path as osp

import PIL
import numpy as np
import scipy.sparse
from lib.config import config as cfg
# from lib.utils.cython_bbox import bbox_overlaps


class imdb(object):
    """Image database."""

    def __init__(self, name, classes=None):
        self._name = name
        self._num_classes = 0
        if not classes:
            self._classes = []
        else:
            self._classes = classes
        self._image_index = []
        self._obj_proposer = 'gt'
        self._roidb = None
        self._roidb_handler = self.default_roidb
        # Use this dict for storing dataset specific config options
        self.config = {}

    @property
    def name(self):
        return self._name

    @property
    def num_classes(self):
        return len(self._classes)

    @property
    def classes(self):
        return self._classes

    @property
    def image_index(self):
        return self._image_index

    @property
    def roidb_handler(self):
        return self._roidb_handler

    @roidb_handler.setter
    def roidb_handler(self, val):
        self._roidb_handler = val

    def set_proposal_method(self, method):
        method = eval('self.' + method + '_roidb')
        self.roidb_handler = method

    @property
    def roidb(self):
        # A roidb is a list of dictionaries, each with the following keys:
        #   boxes
        #   gt_overlaps
        #   gt_classes
        #   flipped
        if self._roidb is not None:
            return self._roidb
        self._roidb = self.roidb_handler()
        return self._roidb

    @property
    def cache_path(self):
        cache_path = osp.abspath(osp.join(cfg.FLAGS2["data_dir"], 'cache'))
        if not os.path.exists(cache_path):
            os.makedirs(cache_path)
        return cache_path

    @property
    def num_images(self):
        return len(self.image_index)

    def image_path_at(self, i):
        raise NotImplementedError

    def default_roidb(self):
        raise NotImplementedError

    def evaluate_detections(self, all_boxes, output_dir=None):
        """
        all_boxes is a list of length number-of-classes.
        Each list element is a list of length number-of-images.
        Each of those list elements is either an empty list []
        or a numpy array of detection.

        all_boxes[class][image] = [] or np.array of shape #dets x 5
        """
        raise NotImplementedError

    def _get_widths(self):
        return [PIL.Image.open(self.image_path_at(i)).size[0]
                for i in range(self.num_images)]

    def append_flipped_images(self):
        num_images = self.num_images
        widths = self._get_widths()
        for i in range(num_images):
            boxes = self.roidb[i]['boxes'].copy()
            oldx1 = boxes[:, 0].copy()
            oldx2 = boxes[:, 2].copy()
            boxes[:, 0] = widths[i] - oldx2 - 1
            boxes[:, 2] = widths[i] - oldx1 - 1
            assert (boxes[:, 2] >= boxes[:, 0]).all()
            entry = {'boxes': boxes,
                     'gt_overlaps': self.roidb[i]['gt_overlaps'],
                     'gt_classes': self.roidb[i]['gt_classes'],
                     'flipped': True}
            self.roidb.append(entry)
        self._image_index = self._image_index * 2

    # def evaluate_recall(self, candidate_boxes=None, thresholds=None,
    #                     area='all', limit=None):
    #     """Evaluate detection proposal recall metrics.
    #
    #     Returns:
    #         results: dictionary of results with keys
    #             'ar': average recall
    #             'recalls': vector recalls at each IoU overlap threshold
    #             'thresholds': vector of IoU overlap thresholds
    #             'gt_overlaps': vector of all ground-truth overlaps
    #     """
    #     # Record max overlap value for each gt box
    #     # Return vector of overlap values
    #     areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3,
    #              '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
    #     area_ranges = [[0 ** 2, 1e5 ** 2],  # all
    #                    [0 ** 2, 32 ** 2],  # small
    #                    [32 ** 2, 96 ** 2],  # medium
    #                    [96 ** 2, 1e5 ** 2],  # large
    #                    [96 ** 2, 128 ** 2],  # 96-128
    #                    [128 ** 2, 256 ** 2],  # 128-256
    #                    [256 ** 2, 512 ** 2],  # 256-512
    #                    [512 ** 2, 1e5 ** 2],  # 512-inf
    #                    ]
    #     assert area in areas, 'unknown area range: {}'.format(area)
    #     area_range = area_ranges[areas[area]]
    #     gt_overlaps = np.zeros(0)
    #     num_pos = 0
    #     for i in range(self.num_images):
    #         # Checking for max_overlaps == 1 avoids including crowd annotations
    #         # (...pretty hacking :/)
    #         max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
    #         gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
    #                            (max_gt_overlaps == 1))[0]
    #         gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
    #         gt_areas = self.roidb[i]['seg_areas'][gt_inds]
    #         valid_gt_inds = np.where((gt_areas >= area_range[0]) &
    #                                  (gt_areas <= area_range[1]))[0]
    #         gt_boxes = gt_boxes[valid_gt_inds, :]
    #         num_pos += len(valid_gt_inds)
    #
    #         if candidate_boxes is None:
    #             # If candidate_boxes is not supplied, the default is to use the
    #             # non-ground-truth boxes from this roidb
    #             non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
    #             boxes = self.roidb[i]['boxes'][non_gt_inds, :]
    #         else:
    #             boxes = candidate_boxes[i]
    #         if boxes.shape[0] == 0:
    #             continue
    #         if limit is not None and boxes.shape[0] > limit:
    #             boxes = boxes[:limit, :]
    #
    #         overlaps = bbox_overlaps(boxes.astype(np.float),
    #                                  gt_boxes.astype(np.float))
    #
    #         _gt_overlaps = np.zeros((gt_boxes.shape[0]))
    #         for j in range(gt_boxes.shape[0]):
    #             # find which proposal box maximally covers each gt box
    #             argmax_overlaps = overlaps.argmax(axis=0)
    #             # and get the iou amount of coverage for each gt box
    #             max_overlaps = overlaps.max(axis=0)
    #             # find which gt box is 'best' covered (i.e. 'best' = most iou)
    #             gt_ind = max_overlaps.argmax()
    #             gt_ovr = max_overlaps.max()
    #             assert (gt_ovr >= 0)
    #             # find the proposal box that covers the best covered gt box
    #             box_ind = argmax_overlaps[gt_ind]
    #             # record the iou coverage of this gt box
    #             _gt_overlaps[j] = overlaps[box_ind, gt_ind]
    #             assert (_gt_overlaps[j] == gt_ovr)
    #             # mark the proposal box and the gt box as used
    #             overlaps[box_ind, :] = -1
    #             overlaps[:, gt_ind] = -1
    #         # append recorded iou coverage level
    #         gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
    #
    #     gt_overlaps = np.sort(gt_overlaps)
    #     if thresholds is None:
    #         step = 0.05
    #         thresholds = np.arange(0.5, 0.95 + 1e-5, step)
    #     recalls = np.zeros_like(thresholds)
    #     # compute recall for each iou threshold
    #     for i, t in enumerate(thresholds):
    #         recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
    #     # ar = 2 * np.trapz(recalls, thresholds)
    #     ar = recalls.mean()
    #     return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
    #             'gt_overlaps': gt_overlaps}

    # def create_roidb_from_box_list(self, box_list, gt_roidb):
    #     assert len(box_list) == self.num_images, \
    #         'Number of boxes must match number of ground-truth images'
    #     roidb = []
    #     for i in range(self.num_images):
    #         boxes = box_list[i]
    #         num_boxes = boxes.shape[0]
    #         overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
    #
    #         if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
    #             gt_boxes = gt_roidb[i]['boxes']
    #             gt_classes = gt_roidb[i]['gt_classes']
    #             gt_overlaps = bbox_overlaps(boxes.astype(np.float),
    #                                         gt_boxes.astype(np.float))
    #             argmaxes = gt_overlaps.argmax(axis=1)
    #             maxes = gt_overlaps.max(axis=1)
    #             I = np.where(maxes > 0)[0]
    #             overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
    #
    #         overlaps = scipy.sparse.csr_matrix(overlaps)
    #         roidb.append({
    #             'boxes': boxes,
    #             'gt_classes': np.zeros((num_boxes,), dtype=np.int32),
    #             'gt_overlaps': overlaps,
    #             'flipped': False,
    #             'seg_areas': np.zeros((num_boxes,), dtype=np.float32),
    #         })
    #     return roidb

    @staticmethod
    def merge_roidbs(a, b):
        assert len(a) == len(b)
        for i in range(len(a)):
            a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
            a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
                                            b[i]['gt_classes']))
            a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
                                                       b[i]['gt_overlaps']])
            a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
                                           b[i]['seg_areas']))
        return a

    def competition_mode(self, on):
        """Turn competition mode on or off."""
        pass
